Summarize large Hyperliquid trader positions

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false) already disclose safety and idempotence. The description adds behavioral insight beyond annotations by revealing the data source is a subset cohort, not a full snapshot, and that positions are aggregated. This provides meaningful context about output limitations not captured in annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no filler, front-loaded with the core action and scope. Every word serves a purpose. The structure is optimal for quick scanning by an AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of 7 parameters (several with enums) and zero schema descriptions, the description is insufficient for the agent to use the tool correctly. It covers purpose but fails to explain parameter selection, defaults, or how to filter results. The output schema exists but does not offset the lack of input guidance. A 2 reflects the significant missing context for a tool with this many parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description provides no parameter explanations whatsoever. Parameters like sort_by, direction, market_type have enums but no textual hints about their effect. The agent must infer meaning purely from names and enum values, which is insufficient for seven parameters, especially with no defaults described for behavior. This is a critical gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool aggregates long and short exposure by market within a specific cohort (Market Depth Lab's rolling monitored-address cohort). It uses a specific verb ('aggregate') and resource ('exposure by market'), and explicitly distinguishes itself from a protocol-wide holder census, leaving no ambiguity about its scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear negative guidance by stating what the tool does not do (not a protocol-wide census or simultaneous snapshot), helping the agent avoid misuse. However, it does not explicitly recommend when to use this tool over siblings like hyperliquid_list_top_position_holders or provide positive usage context. The clarity of exclusion earns a 4.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.